GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults

Abstract

Electrical and mechanical equipment with rotating parts often face the challenge of early breakdown due to defects in the gears or rolling bearings. Automated industrial systems can be significantly impeded by this type of fault in revolving components because of manual fault detection and the additional time required for repairing and replacing them. This research presents GearFaultNet, a novel, lightweight 1D Convolutional Neural Network (CNN)-based network, designed to detect gearbox faults. GearFaultNet can be an effective measure for real-time detection of sudden shutdowns and can alleviate downtime and system losses in the industrial aspect. The proposed framework involves the integration of four-channel vibration data from different loading conditions, which are preprocessed in the temporal domain and fed to GearFaultNet to classify the gearbox's condition as either Healthy or Broken. The developed lightweight deep learning network has achieved higher accuracy than those proposed in existing literature. The overall accuracy achieved by this framework is 94.04%. This shallow network can also be applied to estimate other mechanical faults in different machinery.